Artificial intelligence suite for optimizing industrial plant designs

Abstract

An artificial intelligence search engine integrates engineering knowledge and three-dimensional routing space treatment. A graphical user interface (GUI) prompts a user to input starting and ending points corresponding to conduits of a specified type, via an interactive three-dimensional model of a space in an industrial plant. The artificial intelligence search engine determines an optimal path from multiple three-dimensional path options corresponding to each conduit, based on a design constraint and/or objective. A machine-learning model modifies the optimal path options, based on previous user actions responding to previous optimal path options. The GUI displays the modified path options via the interactive three-dimensional model of the space. A natural language processor executes to: i) query engineering information, ii) operate the artificial intelligence search engine to generate design options, iii) deploy user-selected options, or iv) adapt the modified path options, in response to user actions.

What is claimed is: 
     
         1 . A system for an artificial intelligence suite that optimizes industrial plant designs, the system comprising:
 one or more processors; and   a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to:   integrate, by an artificial intelligence search engine, engineering knowledge and three-dimensional routing space treatment;   prompt, by a graphical user interface, a user to input starting points and ending points corresponding to conduits of a specified type, via an interactive three-dimensional model of a space in an industrial plant;   generate multiple three-dimensional path options from a starting point to an ending point corresponding to each of the conduits;   determine, by the artificial intelligence search engine, an optimal path option from the multiple three-dimensional path options corresponding to each of the conduits, based on at least one of at least one design constraint or at least one design objective;   modify, by a machine-learning model, the optimal path options, based on previous user actions in response to previous optimal path options;   display, via the graphical user interface, the modified path options via the interactive three-dimensional model of the space in the industrial plant; and   execute, in response to user actions, a natural language processor to: i) query engineering information, ii) operate the artificial intelligence search engine to generate design options, iii) deploy user-selected options, or iv) adapt the modified path options.   
     
     
         2 . The system of  claim 1 , wherein engineering knowledge and three-dimensional routing space treatment comprise at least one of practice rules, requirements consideration, design objectives, design constraints, cost targets, or sustainability targets. 
     
     
         3 . The system of  claim 1 , wherein the conduit comprises an appropriate type of channel for conveying at least one of water, a liquid, a gas, electricity, heat, or air conditioning. 
     
     
         4 . The system of  claim 1 , wherein generating the path options for each conduit comprises organizing the path options into compatible sets of path options, with each set ranked by at least one of the at least one design constraint or the at least one design objective. 
     
     
         5 . The system of  claim 1 , wherein at least one of the at least one design constraint or the at least one design objective for the conduits are input via at least one of the graphical user interface or the natural language processor, and the optimal path options are at least one of constructable, sustainable based on layout options, or inherently clash free based on sequencing the processing of the conduits. 
     
     
         6 . The system of  claim 1 , wherein the machine learning model at least one of learns from a reference dataset to guide the artificial intelligence search process, learns from the natural language processor adapting the modified path options, or produces additional relevant paths via at least one of a reliability check, a constructability check, or a feasibility check. 
     
     
         7 . The system of  claim 1 , wherein adapting via the natural language processor comprises adapting at least one of the at least one design constraint or the at least one design objective, and the natural language processor comprises a bi-directional large language model that searches three-dimensional models associated with engineering information, and executes discipline-specific tasks. 
     
     
         8 . A computer-implemented method for visualizing data provided by external sources, the computer-implemented method comprising:
 integrating, by an artificial intelligence search engine, engineering knowledge and three-dimensional routing space treatment;   prompting, by a graphical user interface, a user to input starting points and ending points corresponding to conduits of a specified type, via an interactive three-dimensional model of a space in an industrial plant;   generating multiple three-dimensional path options from a starting point to an ending point corresponding to each of the conduits;   determining, by the artificial intelligence search engine, an optimal path option from the multiple three-dimensional path options corresponding to each of the conduits, based on at least one of at least one design constraint or at least one design objective;   modifying, by a machine-learning model, the optimal path options, based on previous user actions in response to previous optimal path options;   displaying, via the graphical user interface, the modified path options via the interactive three-dimensional model of the space in the industrial plant; and   executing, in response to user actions, a natural language processor to: i) query engineering information, ii) operate the artificial intelligence search engine to generate design options, iii) deploy user-selected options, or iv) adapt the modified path options.   
     
     
         9 . The computer-implemented method of  claim 8 , wherein engineering knowledge and three-dimensional routing space treatment comprise at least one of practice rules, requirements consideration, design objectives, design constraints, cost targets, or sustainability targets. 
     
     
         10 . The computer-implemented method of  claim 8 , wherein the conduit comprises an appropriate type of channel for conveying at least one of water, a liquid, a gas, electricity, heat, or air conditioning. 
     
     
         11 . The computer-implemented method of  claim 8 , wherein generating the path options for each conduit comprises organizing the path options into compatible sets of path options, with each set ranked by at least one of the at least one design constraint or the at least one design objective. 
     
     
         12 . The computer-implemented method of  claim 8 , wherein at least one of the at least one design constraint or the at least one design objective are input via at least one of the graphical user interface or the natural language processor, and the optimal path options are at least one of constructable, sustainable based on layout options, or inherently clash free based on sequencing the processing of the conduits. 
     
     
         13 . The computer-implemented method of  claim 8 , wherein the machine learning model at least one of learns from a reference dataset to guide the artificial intelligence search process, learns from the natural language processor adapting the modified path options, or produces additional relevant paths via at least one of a reliability check, a constructability check, or a feasibility check. 
     
     
         14 . The computer-implemented method of  claim 8 , wherein adapting via the natural language processor comprises adapting at least one of the at least one design constraint or the at least one design objective, and the natural language processor comprises a bi-directional large language model that at least one of searches three-dimensional models associated with engineering information, or executes discipline-specific tasks. 
     
     
         15 . A computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein to be executed by one or more processors, the program code including instructions to:
 integrate, by an artificial intelligence search engine, engineering knowledge and three-dimensional routing space treatment;   prompt, by a graphical user interface, a user to input starting points and ending points corresponding to conduits of a specified type, via an interactive three-dimensional model of a space in an industrial plant;   generate multiple three-dimensional path options from a starting point to an ending point corresponding to each of the conduits;   determine, by the artificial intelligence search engine, an optimal path option from the multiple three-dimensional path options corresponding to each of the conduits, based on at least one of at least one design constraint or at least one design objective;   modify, by a machine-learning model, the optimal path options, based on previous user actions in response to previous optimal path options;   display, via the graphical user interface, the modified path options via the interactive three-dimensional model of the space in the industrial plant; and   execute, in response to user actions, a natural language processor to: i) query engineering information, ii) operate the artificial intelligence search engine to generate design options, iii) deploy user-selected options, or iv) adapt the modified path options.   
     
     
         16 . The computer program product of  claim 15 , wherein engineering knowledge and three-dimensional routing space treatment comprise at least one of practice rules, requirements consideration, design objectives, design constraints, cost targets, or sustainability targets. 
     
     
         17 . The computer program product of  claim 15 , wherein the conduit comprises an appropriate type of channel for conveying at least one of water, a liquid, a gas, electricity, heat, or air conditioning. 
     
     
         18 . The computer program product of  claim 15 , wherein generating the path options for each conduit comprises organizing the path options into compatible sets of path options, with each set ranked by at least one of the at least one design constraint or the at least one design objective, and wherein at least one of the at least one design constraint or the at least one design objective for the conduits are input via at least one of the graphical user interface or the natural language processor, and the optimal path options are at least one of constructable, sustainable based on layout options, or inherently clash free based on sequencing the processing of the conduits. 
     
     
         19 . The computer program product of  claim 15 , wherein the machine learning model at least one of learns from a reference dataset to guide the artificial intelligence search process, learns from the natural language processor adapting the modified path options, or produces additional relevant paths via at least one of a reliability check, a constructability check, or a feasibility check. 
     
     
         20 . The computer program product of  claim 15 , wherein adapting via the natural language processor comprises adapting at least one of the at least one design constraint or the at least one design objective, and the natural language processor comprises a bi-directional large language model that at least one of searches three-dimensional models associated with engineering information, or executes discipline-specific tasks.